{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/max/Documents/tsfresh/venv/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n",
      "/Users/max/Documents/tsfresh/venv/lib/python2.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
     ]
    }
   ],
   "source": [
    "from tsfresh.examples import load_driftbif\n",
    "from tsfresh.feature_extraction import ComprehensiveFCParameters, extract_features\n",
    "from tsfresh.feature_extraction.settings import get_combiner_functions, get_simple_functions\n",
    "import pandas as pd\n",
    "import pprint\n",
    "import timeit\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.000118</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.000184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000309</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  time     value\n",
       "0   0  0.00  0.000000\n",
       "1   0  0.05  0.000118\n",
       "2   0  0.10  0.000184\n",
       "3   0  0.15  0.000309\n",
       "4   0  0.20  0.000373"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "X, _ = load_driftbif(10, 1000)\n",
    "X.drop(\"dimension\", axis=1, inplace=True)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# get all the parameters and respective functions\n",
    "settings = ComprehensiveFCParameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 61/61 [10:37<00:00, 10.44s/it]   \n"
     ]
    }
   ],
   "source": [
    "# compare performance of tsfresh for simple feature calculator that are calculated individually\n",
    "res  = pd.DataFrame()\n",
    "n_ts = 20\n",
    "l_ts = 1000\n",
    "n_ti = 3\n",
    "\n",
    "for f, param in tqdm(settings.items()):\n",
    "    res.loc[f, \"feature\"] = f\n",
    "    res.loc[f, \"n_samp\"] = n_ts\n",
    "    res.loc[f, \"length\"] = l_ts\n",
    "    \n",
    "    fc_dict = {f:param}\n",
    "    \n",
    "    t = timeit.timeit(lambda : extract_features(timeseries_container=X, \n",
    "                                                column_id=\"id\",\n",
    "                                                n_jobs=0, \n",
    "                                                default_fc_parameters=fc_dict, \n",
    "                                                disable_progressbar=True), \n",
    "                      number=n_ti)\n",
    "    n_fs = len(param) if param is not None else 1\n",
    "    res.loc[f, \"n_fs\"] = n_fs\n",
    "    res.loc[f, \"t_abs\"] = t * 1.0/n_fs\n",
    "    res.loc[f, \"t_1ts\"] = t*1.0/(n_ts*n_fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>n_samp</th>\n",
       "      <th>length</th>\n",
       "      <th>n_fs</th>\n",
       "      <th>t_abs</th>\n",
       "      <th>t_1ts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>agg_linear_trend</th>\n",
       "      <td>agg_linear_trend</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.165438</td>\n",
       "      <td>0.008272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>binned_entropy</th>\n",
       "      <td>binned_entropy</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.040902</td>\n",
       "      <td>0.002045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>median</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.035762</td>\n",
       "      <td>0.001788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_location_of_maximum</th>\n",
       "      <td>first_location_of_maximum</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.031573</td>\n",
       "      <td>0.001579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_second_derivate_central</th>\n",
       "      <td>mean_second_derivate_central</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.036605</td>\n",
       "      <td>0.001830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>absolute_sum_of_changes</th>\n",
       "      <td>absolute_sum_of_changes</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.030932</td>\n",
       "      <td>0.001547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minimum</th>\n",
       "      <td>minimum</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.037429</td>\n",
       "      <td>0.001871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_above_mean</th>\n",
       "      <td>count_above_mean</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.042220</td>\n",
       "      <td>0.002111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_cwt_peaks</th>\n",
       "      <td>number_cwt_peaks</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.162668</td>\n",
       "      <td>0.158133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_duplicate_min</th>\n",
       "      <td>has_duplicate_min</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.304100</td>\n",
       "      <td>0.015205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_duplicate_max</th>\n",
       "      <td>has_duplicate_max</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.295066</td>\n",
       "      <td>0.014753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_below_mean</th>\n",
       "      <td>count_below_mean</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033988</td>\n",
       "      <td>0.001699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>augmented_dickey_fuller</th>\n",
       "      <td>augmented_dickey_fuller</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.836813</td>\n",
       "      <td>0.041841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friedrich_coefficients</th>\n",
       "      <td>friedrich_coefficients</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.135099</td>\n",
       "      <td>0.006755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_location_of_minimum</th>\n",
       "      <td>first_location_of_minimum</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.039619</td>\n",
       "      <td>0.001981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>kurtosis</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.052459</td>\n",
       "      <td>0.002623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quantile</th>\n",
       "      <td>quantile</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.020993</td>\n",
       "      <td>0.001050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_value_number_to_time_series_length</th>\n",
       "      <td>ratio_value_number_to_time_series_length</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.046931</td>\n",
       "      <td>0.002347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>approximate_entropy</th>\n",
       "      <td>approximate_entropy</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.766224</td>\n",
       "      <td>0.988311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value_count</th>\n",
       "      <td>value_count</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.016928</td>\n",
       "      <td>0.000846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>range_count</th>\n",
       "      <td>range_count</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.053133</td>\n",
       "      <td>0.002657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cwt_coefficients</th>\n",
       "      <td>cwt_coefficients</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.001868</td>\n",
       "      <td>0.000093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum_of_reoccurring_values</th>\n",
       "      <td>sum_of_reoccurring_values</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.056706</td>\n",
       "      <td>0.002835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_duplicate</th>\n",
       "      <td>has_duplicate</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.055856</td>\n",
       "      <td>0.002793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_entropy</th>\n",
       "      <td>sample_entropy</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>471.309897</td>\n",
       "      <td>23.565495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skewness</th>\n",
       "      <td>skewness</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.059793</td>\n",
       "      <td>0.002990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abs_energy</th>\n",
       "      <td>abs_energy</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.032225</td>\n",
       "      <td>0.001611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_langevin_fixed_point</th>\n",
       "      <td>max_langevin_fixed_point</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.579113</td>\n",
       "      <td>0.028956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_crossing_m</th>\n",
       "      <td>number_crossing_m</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.030456</td>\n",
       "      <td>0.001523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum_of_reoccurring_data_points</th>\n",
       "      <td>sum_of_reoccurring_data_points</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.077683</td>\n",
       "      <td>0.003884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longest_strike_below_mean</th>\n",
       "      <td>longest_strike_below_mean</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.043251</td>\n",
       "      <td>0.002163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_peaks</th>\n",
       "      <td>number_peaks</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.385918</td>\n",
       "      <td>0.019296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum_values</th>\n",
       "      <td>sum_values</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.057641</td>\n",
       "      <td>0.002882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_abs_change</th>\n",
       "      <td>mean_abs_change</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.056831</td>\n",
       "      <td>0.002842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_location_of_maximum</th>\n",
       "      <td>last_location_of_maximum</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.059728</td>\n",
       "      <td>0.002986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index_mass_quantile</th>\n",
       "      <td>index_mass_quantile</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.008016</td>\n",
       "      <td>0.000401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance_larger_than_standard_deviation</th>\n",
       "      <td>variance_larger_than_standard_deviation</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.043785</td>\n",
       "      <td>0.002189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c3</th>\n",
       "      <td>c3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.014765</td>\n",
       "      <td>0.000738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear_trend</th>\n",
       "      <td>linear_trend</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.035071</td>\n",
       "      <td>0.001754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percentage_of_reoccurring_values_to_all_values</th>\n",
       "      <td>percentage_of_reoccurring_values_to_all_values</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.107076</td>\n",
       "      <td>0.005354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_beyond_r_sigma</th>\n",
       "      <td>ratio_beyond_r_sigma</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.351078</td>\n",
       "      <td>0.017554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>large_standard_deviation</th>\n",
       "      <td>large_standard_deviation</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.024175</td>\n",
       "      <td>0.001209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change_quantiles</th>\n",
       "      <td>change_quantiles</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.392062</td>\n",
       "      <td>0.019603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_change</th>\n",
       "      <td>mean_change</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.036554</td>\n",
       "      <td>0.001828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_location_of_minimum</th>\n",
       "      <td>last_location_of_minimum</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.032329</td>\n",
       "      <td>0.001616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_looking</th>\n",
       "      <td>symmetry_looking</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.002663</td>\n",
       "      <td>0.000133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maximum</th>\n",
       "      <td>maximum</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.040453</td>\n",
       "      <td>0.002023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longest_strike_above_mean</th>\n",
       "      <td>longest_strike_above_mean</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.041807</td>\n",
       "      <td>0.002090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percentage_of_reoccurring_datapoints_to_all_datapoints</th>\n",
       "      <td>percentage_of_reoccurring_datapoints_to_all_da...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.042422</td>\n",
       "      <td>0.002121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>length</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.035542</td>\n",
       "      <td>0.001777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>energy_ratio_by_chunks</th>\n",
       "      <td>energy_ratio_by_chunks</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.004319</td>\n",
       "      <td>0.000216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fft_coefficient</th>\n",
       "      <td>fft_coefficient</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>0.000027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_autocorrelation</th>\n",
       "      <td>agg_autocorrelation</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.021451</td>\n",
       "      <td>0.001073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard_deviation</th>\n",
       "      <td>standard_deviation</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.035392</td>\n",
       "      <td>0.001770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>autocorrelation</th>\n",
       "      <td>autocorrelation</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.007387</td>\n",
       "      <td>0.000369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>variance</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.034087</td>\n",
       "      <td>0.001704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial_autocorrelation</th>\n",
       "      <td>partial_autocorrelation</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.007692</td>\n",
       "      <td>0.000385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spkt_welch_density</th>\n",
       "      <td>spkt_welch_density</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.017274</td>\n",
       "      <td>0.000864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_reversal_asymmetry_statistic</th>\n",
       "      <td>time_reversal_asymmetry_statistic</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.018646</td>\n",
       "      <td>0.000932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>mean</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033323</td>\n",
       "      <td>0.001666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                              feature  \\\n",
       "agg_linear_trend                                                                     agg_linear_trend   \n",
       "binned_entropy                                                                         binned_entropy   \n",
       "median                                                                                         median   \n",
       "first_location_of_maximum                                                   first_location_of_maximum   \n",
       "mean_second_derivate_central                                             mean_second_derivate_central   \n",
       "absolute_sum_of_changes                                                       absolute_sum_of_changes   \n",
       "minimum                                                                                       minimum   \n",
       "count_above_mean                                                                     count_above_mean   \n",
       "number_cwt_peaks                                                                     number_cwt_peaks   \n",
       "has_duplicate_min                                                                   has_duplicate_min   \n",
       "has_duplicate_max                                                                   has_duplicate_max   \n",
       "count_below_mean                                                                     count_below_mean   \n",
       "augmented_dickey_fuller                                                       augmented_dickey_fuller   \n",
       "friedrich_coefficients                                                         friedrich_coefficients   \n",
       "first_location_of_minimum                                                   first_location_of_minimum   \n",
       "kurtosis                                                                                     kurtosis   \n",
       "quantile                                                                                     quantile   \n",
       "ratio_value_number_to_time_series_length                     ratio_value_number_to_time_series_length   \n",
       "approximate_entropy                                                               approximate_entropy   \n",
       "value_count                                                                               value_count   \n",
       "range_count                                                                               range_count   \n",
       "cwt_coefficients                                                                     cwt_coefficients   \n",
       "sum_of_reoccurring_values                                                   sum_of_reoccurring_values   \n",
       "has_duplicate                                                                           has_duplicate   \n",
       "sample_entropy                                                                         sample_entropy   \n",
       "skewness                                                                                     skewness   \n",
       "abs_energy                                                                                 abs_energy   \n",
       "max_langevin_fixed_point                                                     max_langevin_fixed_point   \n",
       "number_crossing_m                                                                   number_crossing_m   \n",
       "sum_of_reoccurring_data_points                                         sum_of_reoccurring_data_points   \n",
       "...                                                                                               ...   \n",
       "longest_strike_below_mean                                                   longest_strike_below_mean   \n",
       "number_peaks                                                                             number_peaks   \n",
       "sum_values                                                                                 sum_values   \n",
       "mean_abs_change                                                                       mean_abs_change   \n",
       "last_location_of_maximum                                                     last_location_of_maximum   \n",
       "index_mass_quantile                                                               index_mass_quantile   \n",
       "variance_larger_than_standard_deviation                       variance_larger_than_standard_deviation   \n",
       "c3                                                                                                 c3   \n",
       "linear_trend                                                                             linear_trend   \n",
       "percentage_of_reoccurring_values_to_all_values         percentage_of_reoccurring_values_to_all_values   \n",
       "ratio_beyond_r_sigma                                                             ratio_beyond_r_sigma   \n",
       "large_standard_deviation                                                     large_standard_deviation   \n",
       "change_quantiles                                                                     change_quantiles   \n",
       "mean_change                                                                               mean_change   \n",
       "last_location_of_minimum                                                     last_location_of_minimum   \n",
       "symmetry_looking                                                                     symmetry_looking   \n",
       "maximum                                                                                       maximum   \n",
       "longest_strike_above_mean                                                   longest_strike_above_mean   \n",
       "percentage_of_reoccurring_datapoints_to_all_dat...  percentage_of_reoccurring_datapoints_to_all_da...   \n",
       "length                                                                                         length   \n",
       "energy_ratio_by_chunks                                                         energy_ratio_by_chunks   \n",
       "fft_coefficient                                                                       fft_coefficient   \n",
       "agg_autocorrelation                                                               agg_autocorrelation   \n",
       "standard_deviation                                                                 standard_deviation   \n",
       "autocorrelation                                                                       autocorrelation   \n",
       "variance                                                                                     variance   \n",
       "partial_autocorrelation                                                       partial_autocorrelation   \n",
       "spkt_welch_density                                                                 spkt_welch_density   \n",
       "time_reversal_asymmetry_statistic                                   time_reversal_asymmetry_statistic   \n",
       "mean                                                                                             mean   \n",
       "\n",
       "                                                    n_samp  length   n_fs  \\\n",
       "agg_linear_trend                                      20.0  1000.0   48.0   \n",
       "binned_entropy                                        20.0  1000.0    1.0   \n",
       "median                                                20.0  1000.0    1.0   \n",
       "first_location_of_maximum                             20.0  1000.0    1.0   \n",
       "mean_second_derivate_central                          20.0  1000.0    1.0   \n",
       "absolute_sum_of_changes                               20.0  1000.0    1.0   \n",
       "minimum                                               20.0  1000.0    1.0   \n",
       "count_above_mean                                      20.0  1000.0    1.0   \n",
       "number_cwt_peaks                                      20.0  1000.0    2.0   \n",
       "has_duplicate_min                                     20.0  1000.0    1.0   \n",
       "has_duplicate_max                                     20.0  1000.0    1.0   \n",
       "count_below_mean                                      20.0  1000.0    1.0   \n",
       "augmented_dickey_fuller                               20.0  1000.0    3.0   \n",
       "friedrich_coefficients                                20.0  1000.0    4.0   \n",
       "first_location_of_minimum                             20.0  1000.0    1.0   \n",
       "kurtosis                                              20.0  1000.0    1.0   \n",
       "quantile                                              20.0  1000.0    8.0   \n",
       "ratio_value_number_to_time_series_length              20.0  1000.0    1.0   \n",
       "approximate_entropy                                   20.0  1000.0    5.0   \n",
       "value_count                                           20.0  1000.0    5.0   \n",
       "range_count                                           20.0  1000.0    1.0   \n",
       "cwt_coefficients                                      20.0  1000.0   60.0   \n",
       "sum_of_reoccurring_values                             20.0  1000.0    1.0   \n",
       "has_duplicate                                         20.0  1000.0    1.0   \n",
       "sample_entropy                                        20.0  1000.0    1.0   \n",
       "skewness                                              20.0  1000.0    1.0   \n",
       "abs_energy                                            20.0  1000.0    1.0   \n",
       "max_langevin_fixed_point                              20.0  1000.0    1.0   \n",
       "number_crossing_m                                     20.0  1000.0    3.0   \n",
       "sum_of_reoccurring_data_points                        20.0  1000.0    1.0   \n",
       "...                                                    ...     ...    ...   \n",
       "longest_strike_below_mean                             20.0  1000.0    1.0   \n",
       "number_peaks                                          20.0  1000.0    5.0   \n",
       "sum_values                                            20.0  1000.0    1.0   \n",
       "mean_abs_change                                       20.0  1000.0    1.0   \n",
       "last_location_of_maximum                              20.0  1000.0    1.0   \n",
       "index_mass_quantile                                   20.0  1000.0    8.0   \n",
       "variance_larger_than_standard_deviation               20.0  1000.0    1.0   \n",
       "c3                                                    20.0  1000.0    3.0   \n",
       "linear_trend                                          20.0  1000.0    5.0   \n",
       "percentage_of_reoccurring_values_to_all_values        20.0  1000.0    1.0   \n",
       "ratio_beyond_r_sigma                                  20.0  1000.0   10.0   \n",
       "large_standard_deviation                              20.0  1000.0   19.0   \n",
       "change_quantiles                                      20.0  1000.0  100.0   \n",
       "mean_change                                           20.0  1000.0    1.0   \n",
       "last_location_of_minimum                              20.0  1000.0    1.0   \n",
       "symmetry_looking                                      20.0  1000.0   20.0   \n",
       "maximum                                               20.0  1000.0    1.0   \n",
       "longest_strike_above_mean                             20.0  1000.0    1.0   \n",
       "percentage_of_reoccurring_datapoints_to_all_dat...    20.0  1000.0    1.0   \n",
       "length                                                20.0  1000.0    1.0   \n",
       "energy_ratio_by_chunks                                20.0  1000.0   10.0   \n",
       "fft_coefficient                                       20.0  1000.0  400.0   \n",
       "agg_autocorrelation                                   20.0  1000.0    3.0   \n",
       "standard_deviation                                    20.0  1000.0    1.0   \n",
       "autocorrelation                                       20.0  1000.0   10.0   \n",
       "variance                                              20.0  1000.0    1.0   \n",
       "partial_autocorrelation                               20.0  1000.0   10.0   \n",
       "spkt_welch_density                                    20.0  1000.0    3.0   \n",
       "time_reversal_asymmetry_statistic                     20.0  1000.0    3.0   \n",
       "mean                                                  20.0  1000.0    1.0   \n",
       "\n",
       "                                                         t_abs      t_1ts  \n",
       "agg_linear_trend                                      0.165438   0.008272  \n",
       "binned_entropy                                        0.040902   0.002045  \n",
       "median                                                0.035762   0.001788  \n",
       "first_location_of_maximum                             0.031573   0.001579  \n",
       "mean_second_derivate_central                          0.036605   0.001830  \n",
       "absolute_sum_of_changes                               0.030932   0.001547  \n",
       "minimum                                               0.037429   0.001871  \n",
       "count_above_mean                                      0.042220   0.002111  \n",
       "number_cwt_peaks                                      3.162668   0.158133  \n",
       "has_duplicate_min                                     0.304100   0.015205  \n",
       "has_duplicate_max                                     0.295066   0.014753  \n",
       "count_below_mean                                      0.033988   0.001699  \n",
       "augmented_dickey_fuller                               0.836813   0.041841  \n",
       "friedrich_coefficients                                0.135099   0.006755  \n",
       "first_location_of_minimum                             0.039619   0.001981  \n",
       "kurtosis                                              0.052459   0.002623  \n",
       "quantile                                              0.020993   0.001050  \n",
       "ratio_value_number_to_time_series_length              0.046931   0.002347  \n",
       "approximate_entropy                                  19.766224   0.988311  \n",
       "value_count                                           0.016928   0.000846  \n",
       "range_count                                           0.053133   0.002657  \n",
       "cwt_coefficients                                      0.001868   0.000093  \n",
       "sum_of_reoccurring_values                             0.056706   0.002835  \n",
       "has_duplicate                                         0.055856   0.002793  \n",
       "sample_entropy                                      471.309897  23.565495  \n",
       "skewness                                              0.059793   0.002990  \n",
       "abs_energy                                            0.032225   0.001611  \n",
       "max_langevin_fixed_point                              0.579113   0.028956  \n",
       "number_crossing_m                                     0.030456   0.001523  \n",
       "sum_of_reoccurring_data_points                        0.077683   0.003884  \n",
       "...                                                        ...        ...  \n",
       "longest_strike_below_mean                             0.043251   0.002163  \n",
       "number_peaks                                          0.385918   0.019296  \n",
       "sum_values                                            0.057641   0.002882  \n",
       "mean_abs_change                                       0.056831   0.002842  \n",
       "last_location_of_maximum                              0.059728   0.002986  \n",
       "index_mass_quantile                                   0.008016   0.000401  \n",
       "variance_larger_than_standard_deviation               0.043785   0.002189  \n",
       "c3                                                    0.014765   0.000738  \n",
       "linear_trend                                          0.035071   0.001754  \n",
       "percentage_of_reoccurring_values_to_all_values        0.107076   0.005354  \n",
       "ratio_beyond_r_sigma                                  0.351078   0.017554  \n",
       "large_standard_deviation                              0.024175   0.001209  \n",
       "change_quantiles                                      0.392062   0.019603  \n",
       "mean_change                                           0.036554   0.001828  \n",
       "last_location_of_minimum                              0.032329   0.001616  \n",
       "symmetry_looking                                      0.002663   0.000133  \n",
       "maximum                                               0.040453   0.002023  \n",
       "longest_strike_above_mean                             0.041807   0.002090  \n",
       "percentage_of_reoccurring_datapoints_to_all_dat...    0.042422   0.002121  \n",
       "length                                                0.035542   0.001777  \n",
       "energy_ratio_by_chunks                                0.004319   0.000216  \n",
       "fft_coefficient                                       0.000543   0.000027  \n",
       "agg_autocorrelation                                   0.021451   0.001073  \n",
       "standard_deviation                                    0.035392   0.001770  \n",
       "autocorrelation                                       0.007387   0.000369  \n",
       "variance                                              0.034087   0.001704  \n",
       "partial_autocorrelation                               0.007692   0.000385  \n",
       "spkt_welch_density                                    0.017274   0.000864  \n",
       "time_reversal_asymmetry_statistic                     0.018646   0.000932  \n",
       "mean                                                  0.033323   0.001666  \n",
       "\n",
       "[61 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "res[\"feature\"] = res.feature.astype(str)\n",
    "res = res.sort_values(by=\"feature\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QFhbGv/71r7vaLVq0iODgYAoLC1GpVHzyySfY2dk98s9aAEyYMIEPP/wQc3NzqlevTocO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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c58490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 20))\n",
    "sns.barplot(y=\"feature\", x=\"t_abs\", data=res)\n",
    "plt.title(\"Runtime of 1 apply features for 1 time series of length 1000\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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F9erVe6T97tChAwcPHtTfvbl371569epFQUFBmW2cnJwwNjZm3759wL2Z9JSUFH3B9CTH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D17ll27dgFgamrK8uXLCQ4OLtEfIYQQVUNW4sRTRaFQkJeXx+TJk6lbty4FBQUUFRUB0Lx5c+CP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\nUhEnIiIiYodUxImIiIjYof8P4q3TEBlDBZYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c86050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,20))\n",
    "sns.barplot(y=\"feature\", x=\"t_1ts\", data=res)\n",
    "plt.title(\"Runtime of aggregate features for 1 time series of length 1000\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "source": [
    "sample_entropy and approximate_entropy make up for most of the runtime, we create the following plots without them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L27ZtX0j7Ff1nt+zsLMjIyC7vGCWq6BklX+lV9IySr/SeNaOdXdE334HMxIm/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\nRZyIiIiIHfr/ggaCKW02EaoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a84cb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,20))\n",
    "sns.barplot(y=\"feature\", x=\"t_1ts\", data=res.drop([\"sample_entropy\", \"approximate_entropy\"]))\n",
    "plt.title(\"Runtime of aggregate features for 1 time series of length 1000\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fft_coefficient                                            0.000027\n",
       "cwt_coefficients                                           0.000093\n",
       "symmetry_looking                                           0.000133\n",
       "energy_ratio_by_chunks                                     0.000216\n",
       "autocorrelation                                            0.000369\n",
       "partial_autocorrelation                                    0.000385\n",
       "index_mass_quantile                                        0.000401\n",
       "c3                                                         0.000738\n",
       "value_count                                                0.000846\n",
       "spkt_welch_density                                         0.000864\n",
       "time_reversal_asymmetry_statistic                          0.000932\n",
       "quantile                                                   0.001050\n",
       "agg_autocorrelation                                        0.001073\n",
       "large_standard_deviation                                   0.001209\n",
       "number_crossing_m                                          0.001523\n",
       "absolute_sum_of_changes                                    0.001547\n",
       "first_location_of_maximum                                  0.001579\n",
       "abs_energy                                                 0.001611\n",
       "last_location_of_minimum                                   0.001616\n",
       "mean                                                       0.001666\n",
       "count_below_mean                                           0.001699\n",
       "variance                                                   0.001704\n",
       "linear_trend                                               0.001754\n",
       "standard_deviation                                         0.001770\n",
       "length                                                     0.001777\n",
       "median                                                     0.001788\n",
       "mean_change                                                0.001828\n",
       "mean_second_derivate_central                               0.001830\n",
       "minimum                                                    0.001871\n",
       "first_location_of_minimum                                  0.001981\n",
       "                                                            ...    \n",
       "binned_entropy                                             0.002045\n",
       "longest_strike_above_mean                                  0.002090\n",
       "count_above_mean                                           0.002111\n",
       "percentage_of_reoccurring_datapoints_to_all_datapoints     0.002121\n",
       "longest_strike_below_mean                                  0.002163\n",
       "variance_larger_than_standard_deviation                    0.002189\n",
       "ar_coefficient                                             0.002235\n",
       "ratio_value_number_to_time_series_length                   0.002347\n",
       "kurtosis                                                   0.002623\n",
       "range_count                                                0.002657\n",
       "has_duplicate                                              0.002793\n",
       "sum_of_reoccurring_values                                  0.002835\n",
       "mean_abs_change                                            0.002842\n",
       "sum_values                                                 0.002882\n",
       "last_location_of_maximum                                   0.002986\n",
       "skewness                                                   0.002990\n",
       "sum_of_reoccurring_data_points                             0.003884\n",
       "percentage_of_reoccurring_values_to_all_values             0.005354\n",
       "friedrich_coefficients                                     0.006755\n",
       "agg_linear_trend                                           0.008272\n",
       "has_duplicate_max                                          0.014753\n",
       "has_duplicate_min                                          0.015205\n",
       "ratio_beyond_r_sigma                                       0.017554\n",
       "number_peaks                                               0.019296\n",
       "change_quantiles                                           0.019603\n",
       "max_langevin_fixed_point                                   0.028956\n",
       "augmented_dickey_fuller                                    0.041841\n",
       "number_cwt_peaks                                           0.158133\n",
       "approximate_entropy                                        0.988311\n",
       "sample_entropy                                            23.565495\n",
       "Name: t_1ts, Length: 61, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we calculate how much many features cause 90% of the runtime \n",
    "r = res.t_1ts.copy()\n",
    "r.sort_values(ascending=True, inplace=True)\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fft_coefficient                                            0.000109\n",
       "cwt_coefficients                                           0.000374\n",
       "symmetry_looking                                           0.000533\n",
       "energy_ratio_by_chunks                                     0.000865\n",
       "autocorrelation                                            0.001479\n",
       "partial_autocorrelation                                    0.001540\n",
       "index_mass_quantile                                        0.001605\n",
       "c3                                                         0.002956\n",
       "value_count                                                0.003390\n",
       "spkt_welch_density                                         0.003459\n",
       "time_reversal_asymmetry_statistic                          0.003733\n",
       "quantile                                                   0.004203\n",
       "agg_autocorrelation                                        0.004295\n",
       "large_standard_deviation                                   0.004841\n",
       "number_crossing_m                                          0.006098\n",
       "absolute_sum_of_changes                                    0.006194\n",
       "first_location_of_maximum                                  0.006322\n",
       "abs_energy                                                 0.006452\n",
       "last_location_of_minimum                                   0.006473\n",
       "mean                                                       0.006672\n",
       "count_below_mean                                           0.006805\n",
       "variance                                                   0.006825\n",
       "linear_trend                                               0.007022\n",
       "standard_deviation                                         0.007087\n",
       "length                                                     0.007117\n",
       "median                                                     0.007161\n",
       "mean_change                                                0.007319\n",
       "mean_second_derivate_central                               0.007329\n",
       "minimum                                                    0.007494\n",
       "first_location_of_minimum                                  0.007933\n",
       "                                                            ...    \n",
       "binned_entropy                                             0.008190\n",
       "longest_strike_above_mean                                  0.008371\n",
       "count_above_mean                                           0.008454\n",
       "percentage_of_reoccurring_datapoints_to_all_datapoints     0.008494\n",
       "longest_strike_below_mean                                  0.008660\n",
       "variance_larger_than_standard_deviation                    0.008767\n",
       "ar_coefficient                                             0.008950\n",
       "ratio_value_number_to_time_series_length                   0.009397\n",
       "kurtosis                                                   0.010504\n",
       "range_count                                                0.010639\n",
       "has_duplicate                                              0.011184\n",
       "sum_of_reoccurring_values                                  0.011354\n",
       "mean_abs_change                                            0.011379\n",
       "sum_values                                                 0.011541\n",
       "last_location_of_maximum                                   0.011959\n",
       "skewness                                                   0.011972\n",
       "sum_of_reoccurring_data_points                             0.015554\n",
       "percentage_of_reoccurring_values_to_all_values             0.021440\n",
       "friedrich_coefficients                                     0.027051\n",
       "agg_linear_trend                                           0.033126\n",
       "has_duplicate_max                                          0.059081\n",
       "has_duplicate_min                                          0.060890\n",
       "ratio_beyond_r_sigma                                       0.070297\n",
       "number_peaks                                               0.077273\n",
       "change_quantiles                                           0.078503\n",
       "max_langevin_fixed_point                                   0.115956\n",
       "augmented_dickey_fuller                                    0.167555\n",
       "number_cwt_peaks                                           0.633263\n",
       "approximate_entropy                                        3.957801\n",
       "sample_entropy                                            94.370607\n",
       "Name: t_1ts, Length: 61, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in percentage\n",
    "r / r.sum()*100"
   ]
  }
 ],
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